{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A function to plot linear regression fits. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> from mlxtend.plotting import plot_linear_regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `plot_linear_regression` is a convenience function that uses scikit-learn's `linear_model.LinearRegression` to fit a linear model and SciPy's `stats.pearsonr` to calculate the correlation coefficient. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "- -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1 - Ordinary Least Squares Simple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Sw5AhQ6hSpQq+vr40btzY5UMtPT///DODBw/m1ltvpUSJElStWpWhQ4e6NH9P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\npEns2PETjhw5wtGjR2NdG2A8xSIiIuOPmoIxctN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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10df67b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mlxtend.plotting import plot_linear_regression\n",
    "import numpy as np\n",
    "\n",
    "X = np.array([4, 8, 13, 26, 31, 10, 8, 30, 18, 12, 20, 5, 28, 18, 6, 31, 12,\n",
    "   12, 27, 11, 6, 14, 25, 7, 13,4, 15, 21, 15])\n",
    "\n",
    "y = np.array([14, 24, 22, 59, 66, 25, 18, 60, 39, 32, 53, 18, 55, 41, 28, 61, 35,\n",
    "   36, 52, 23, 19, 25, 73, 16, 32, 14, 31, 43, 34])\n",
    "\n",
    "intercept, slope, corr_coeff = plot_linear_regression(X, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## plot_linear_regression\n",
      "\n",
      "*plot_linear_regression(X, y, model=LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False), corr_func='pearsonr', scattercolor='blue', fit_style='k--', legend=True, xlim='auto')*\n",
      "\n",
      "Plot a linear regression line fit.\n",
      "\n",
      "**Parameters**\n",
      "\n",
      "- `X` : numpy array, shape = [n_samples,]\n",
      "\n",
      "    Samples.\n",
      "\n",
      "- `y` : numpy array, shape (n_samples,)\n",
      "\n",
      "    Target values\n",
      "    model: object (default: sklearn.linear_model.LinearRegression)\n",
      "    Estimator object for regression. Must implement\n",
      "    a .fit() and .predict() method.\n",
      "    corr_func: str or function (default: 'pearsonr')\n",
      "    Uses `pearsonr` from scipy.stats if corr_func='pearsonr'.\n",
      "    to compute the regression slope. If not 'pearsonr', the `corr_func`,\n",
      "    the `corr_func` parameter expects a function of the form\n",
      "    func(<x-array>, <y-array>) as inputs, which is expected to return\n",
      "    a tuple `(<correlation_coefficient>, <some_unused_value>)`.\n",
      "    scattercolor: string (default: blue)\n",
      "    Color of scatter plot points.\n",
      "    fit_style: string (default: k--)\n",
      "    Style for the line fit.\n",
      "    legend: bool (default: True)\n",
      "    Plots legend with corr_coeff coef.,\n",
      "    fit coef., and intercept values.\n",
      "    xlim: array-like (x_min, x_max) or 'auto' (default: 'auto')\n",
      "    X-axis limits for the linear line fit.\n",
      "\n",
      "**Returns**\n",
      "\n",
      "- `regression_fit` : tuple\n",
      "\n",
      "    intercept, slope, corr_coeff (float, float, float)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../../api_modules/mlxtend.plotting/plot_linear_regression.md', 'r') as f:\n",
    "    print(f.read())"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
